Multi-node affinity-based examination for computer network security remediation

ABSTRACT

Multi-node affinity-based examination for computer network security remediation is provided herein. Exemplary methods may include receiving a query that includes a selection of Internet protocol (IP) addresses belonging to nodes within a network, obtaining characteristics for the nodes, determining communications between the nodes and communications between the nodes and any other nodes not included in the selection, determining a primary affinity indicative of communication between the nodes and a secondary affinity indicative of communication between the nodes and the other nodes not included in the selection, and generating a graphical user interface (GUI) that includes representations of the nodes in the range and the other nodes outside the range, placing links between the nodes in the selection and the other nodes not included in the selection based on the primary affinity and the secondary affinity, and providing the graphical user interface to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 15/090,523, filed on Apr. 4, 2016, which claims the benefit of U.S. Provisional Application No. 62/289,053 filed Jan. 29, 2016, all of which are hereby incorporated by reference herein in their entireties including all references and appendices cited therein as if fully incorporated.

FIELD OF THE INVENTION

The present technology pertains to computer security, and more specifically to computer network security.

BACKGROUND ART

A hardware firewall is a network security system that controls incoming and outgoing network traffic. A hardware firewall generally creates a barrier between an internal network (assumed to be trusted and secure) and another network (e.g., the Internet) that is assumed not to be trusted and secure.

Attackers breach internal networks to steal critical data. For example, attackers target low-profile assets to enter the internal network. Inside the internal network and behind the hardware firewall, attackers move laterally across the internal network, exploiting East-West traffic flows, to critical enterprise assets. Once there, attackers siphon off valuable company and customer data.

SUMMARY OF THE INVENTION

Some embodiments of the present invention include computer-implemented methods and apparatuses for multi-node affinity-based examination for computer network security remediation. Exemplary methods may include: receiving a first identifier associated with a first node; retrieving first metadata using the first identifier; identifying a second node in communication with the first node using the first metadata; ascertaining a first characteristic of each first communication between the first and second nodes using the first metadata; examining each first communication for malicious behavior using the first characteristic; receiving a first risk score for each first communication responsive to the examining; and determining that the first risk score associated with one of the second communications exceeds a first predetermined threshold and indicating the first and second nodes are malicious.

Exemplary methods may further include: retrieving second metadata using a second identifier associated with the second node; identifying a third node in communication with the second node using the second metadata; ascertaining a second characteristic of each second communication between the second and third nodes using the second metadata; examining each second communication for malicious behavior using the second characteristic; receiving a second risk score for each second communication responsive to the examining; determining that the second risk score associated with one of the second communications exceeds the first predetermined threshold and indicating that the third node is malicious; providing the identified malicious nodes and communications originating from or directed to the malicious nodes, such that the progress of a security breach or intrusion through the identified malicious nodes and communications is indicated; and remediating the security breach.

Exemplary methods may include: receiving a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network; obtaining characteristics for the nodes; determining communications between the nodes and communications between the nodes and any other nodes not included in the selection of IP addresses; determining a primary affinity indicative of communication between the nodes and a secondary affinity indicative of communication between the nodes and the other nodes not included in the selection of IP addresses; generating a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses; placing links between the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity; and providing the graphical user interface to a user.

Exemplary systems may include: (a) a query processing module that: receives a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network; (b) a data gathering module that: obtains characteristics for the nodes; and determines communications between the nodes, and communications between the nodes and any other nodes not included in the selection of IP addresses; (c) an affinity analysis module that: determines a primary affinity indicative of communication between the nodes and a secondary affinity indicative of communication between the nodes and the other nodes not included in the selection of IP addresses; and (d) a graphics engine that: generates a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses; places links between the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity; and provides the graphical user interface to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed disclosure, and explain various principles and advantages of those embodiments. The methods and systems disclosed herein have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

FIG. 1 is a simplified block diagram of an (physical) environment, according to some embodiments.

FIG. 2 is simplified block diagram of an (virtual) environment, in accordance with some embodiments.

FIG. 3 is simplified block diagram of an environment, according to various embodiments.

FIG. 4 is a simplified block diagram of an environment, in accordance with various embodiments.

FIG. 5 is a simplified block diagram of a system, according to some embodiments.

FIGS. 6A and 6B are a simplified block diagram of an environment, in accordance with some embodiments.

FIGS. 7A and 7B are a flow diagram, according to various embodiments.

FIG. 8 is a graphical representation of malicious relationships, in accordance with various embodiments.

FIG. 9 is a simplified block diagram of a computing system, according to some embodiments.

FIG. 10 is a flowchart of an example method of providing an interactive graphical user interface (GUI) that represents network node affinity and relationships.

FIG. 11 illustrates an example interactive graphical user interface (GUI) that includes nodes of a network.

FIG. 12 illustrates the interactive graphical user interface (GUI) of FIG. 11 after user interactions have been received.

FIG. 13 illustrates an example interactive graphical user interface (GUI) that includes nodes of a network.

FIG. 14 illustrates automatic resizing of the interactive graphical user interface (GUI) of FIG. 13.

FIG. 15 is a simplified block diagram of an example system that can be used to practice aspects of the present disclosure.

DETAILED DESCRIPTION

While this technology is susceptible of embodiment in many different forms, there is shown in the drawings and will herein be described in detail several specific embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the technology and is not intended to limit the technology to the embodiments illustrated. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the technology. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that like or analogous elements and/or components, referred to herein, may be identified throughout the drawings with like reference characters. It will be further understood that several of the figures are merely schematic representations of the present technology. As such, some of the components may have been distorted from their actual scale for pictorial clarity.

Information technology (IT) organizations face cyber threats and advanced attacks. Firewalls are an important part of network security. Firewalls control incoming and outgoing network traffic using a rule set. A rule, for example, allows a connection to a specific (Internet Protocol (IP)) address (and/or port), allows a connection to a specific (IP) address (and/or port) if the connection is secured (e.g., using Internet Protocol security (IPsec)), blocks a connection to a specific (IP) address (and/or port), redirects a connection from one IP address (and/or port) to another IP address (and/or port), logs communications to and/or from a specific IP address (and/or port), and the like. A firewall rule at a low level of abstraction may indicate a specific (IP) address and protocol to which connections are allowed and/or not allowed.

Managing a set of firewall rules is a difficult challenge. Some IT security organizations have a large staff (e.g., dozens of staff members) dedicated to maintaining firewall policy (e.g., a firewall rule set). A firewall rule set can have tens of thousands or even hundreds of thousands of rules. Some embodiments of the present technology may autonomically generate a reliable declarative security policy at a high level of abstraction. Abstraction is a technique for managing complexity by establishing a level of complexity which suppresses the more complex details below the current level. The high-level declarative policy may be compiled to produce a firewall rule set at a low level of abstraction.

FIG. 1 illustrates a system 100 according to some embodiments. System 100 includes network 110 and data center 120. In various embodiments, data center 120 includes firewall 130, optional core switch/router (also referred to as a core device) 140, Top of Rack (ToR) switches 150 ₁-150 _(x), and physical hosts 160 _(1,1)-160 _(x,y).

Network 110 (also referred to as a computer network or data network) is a telecommunications network that allows computers to exchange data. For example, in network 110, networked computing devices pass data to each other along data connections (e.g., network links). Data can be transferred in the form of packets. The connections between nodes may be established using either cable media or wireless media. For example, network 110 includes at least one of a local area network (LAN), wireless local area network (WLAN), wide area network (WAN), metropolitan area network (MAN), and the like. In some embodiments, network 110 includes the Internet.

Data center 120 is a facility used to house computer systems and associated components. Data center 120, for example, comprises computing resources for cloud computing services or operated for the benefit of a particular organization. Data center equipment, for example, is generally mounted in rack cabinets, which are usually placed in single rows forming corridors (e.g., aisles) between them. Firewall 130 creates a barrier between data center 120 and network 110 by controlling incoming and outgoing network traffic based on a rule set.

Optional core switch/router 140 is a high-capacity switch/router that serves as a gateway to network 110 and provides communications between ToR switches 150 ₁ and 150 _(x), and between ToR switches 150 ₁ and 150 _(x) and network 110. ToR switches 150 ₁ and 150 _(x) connect physical hosts 160 _(1,1)-160 _(1,y) and 160 _(x,1)-160 _(x,y) (respectively) together and to network 110 (optionally through core switch/router 140). For example, ToR switches 150 ₁-150 _(x) use a form of packet switching to forward data to a destination physical host (of physical hosts 160 _(1,1)-160 _(x,y)) and (only) transmit a received message to the physical host for which the message was intended.

In some embodiments, physical hosts 160 _(1,1)-160 _(x,y) are computing devices that act as computing servers such as blade servers. Computing devices are described further in relation to FIG. 7. For example, physical hosts 160 _(1,1)-160 _(x,y) comprise physical servers performing the operations described herein, which can be referred to as a bare-metal server environment. Additionally or alternatively, physical hosts 160 _(1,1)-160 _(x,y) may be a part of a cloud computing environment. Cloud computing environments are described further in relation to FIG. 7. By way of further non-limiting example, physical hosts 160 _(1,1)-160 _(x,y) can host different combinations and permutations of virtual and container environments (which can be referred to as a virtualization environment), which are described further below in relation to FIGS. 2-4.

FIG. 2 depicts (virtual) environment 200 according to various embodiments. In some embodiments, environment 200 is implemented in at least one of physical hosts 160 _(1,1)-160 _(x,y) (FIG. 1). Environment 200 includes hardware 210, host operating system (OS) 220, hypervisor 230, and virtual machines (VMs) 260 ₁-260 _(V). In some embodiments, hardware 210 is implemented in at least one of physical hosts 160 _(1,1)-160 _(x,y) (FIG. 1). Host operating system 220 can run on hardware 210 and can also be referred to as the host kernel. Hypervisor 230 optionally includes virtual switch 240 and includes enforcement points 250 ₁-250 _(V). VMs 260 ₁-260 _(V) each include a respective one of operating systems (OSes) 270 ₁-270 _(V) and applications (APPs) 280 ₁-280 _(V).

Hypervisor (also known as a virtual machine monitor (VMM)) 230 is software running on at least one of physical hosts 160 _(1,1)-160 _(x,y), and hypervisor 230 runs VMs 260 ₁-260 _(V). A physical host (of physical hosts 160 _(1,1)-160 _(x,y)) on which hypervisor 230 is running one or more virtual machines 260 ₁-260 _(V), is also referred to as a host machine. Each VM can also be referred to as a guest machine.

For example, hypervisor 230 allows multiple OSes 270 ₁-270 _(V) to share a single physical host (of physical hosts 160 _(1,1)-160 _(x,y)). Each of OSes 270 ₁-270 _(V) appears to have the host machine's processor, memory, and other resources all to itself. However, hypervisor 230 actually controls the host machine's processor and resources, allocating what is needed to each operating system in turn and making sure that the guest OSes (e.g., virtual machines 260 ₁-260 _(V)) cannot disrupt each other. OSes 270 ₁-270 _(V) are described further in relation to FIG. 7.

VMs 260 ₁-260 _(V) also include applications 280 ₁-280 _(V). Applications (and/or services) 280 ₁-280 _(V) are programs designed to carry out operations for a specific purpose. Applications 280 ₁-280 _(V) can include at least one of web application (also known as web apps), web server, transaction processing, database, and the like software. Applications 280 ₁-280 _(V) run using a respective OS of OSes 270 ₁-270 _(V).

Hypervisor 230 optionally includes virtual switch 240. Virtual switch 240 is a logical switching fabric for networking VMs 260 ₁-260 _(V). For example, virtual switch 240 is a program running on a physical host (of physical hosts 160 _(1,1)-160 _(x,y)) that allows a VM (of VMs 260 ₁-260 _(V)) to communicate with another VM.

Hypervisor 230 also includes enforcement points 250 ₁-250 _(V), according to some embodiments. For example, enforcement points 250 ₁-250 _(V) are a firewall service that provides network traffic filtering and monitoring for VMs 260 ₁-260 _(V) and containers (described below in relation to FIGS. 3 and 4). Enforcement points 250 ₁-250 _(V) are described further in related United States Patent Application “Methods and Systems for Orchestrating Physical and Virtual Switches to Enforce Security Boundaries” (application Ser. No. 14/677,827) filed Apr. 2, 2015, which is hereby incorporated by reference for all purposes. Although enforcement points 250 ₁-250 _(V) are shown in hypervisor 230, enforcement points 250 ₁-250 _(V) can additionally or alternatively be realized in one or more containers (described below in relation to FIGS. 3 and 4).

According to some embodiments, enforcement points 250 ₁-250 _(V) control network traffic to and from a VM (of VMs 260 ₁-260 _(V)) (and/or a container) using a rule set. A rule, for example, allows a connection to a specific (IP) address, allows a connection to a specific (IP) address if the connection is secured (e.g., using IPsec), denies a connection to a specific (IP) address, redirects a connection from one IP address to another IP address (e.g., to a honeypot or tar pit), logs communications to and/or from a specific IP address, and the like. Each address is virtual, physical, or both. Connections are incoming to the respective VM (or a container), outgoing from the respective VM (or container), or both. Redirection is described further in related United States Patent Application “System and Method for Threat-Driven Security Policy Controls” (application Ser. No. 14/673,679) filed Mar. 30, 2015, which is hereby incorporated by reference for all purposes.

In some embodiments logging includes metadata associated with action taken by an enforcement point (of enforcement points 250 ₁-250 _(V)), such as the permit, deny, and log behaviors. For example, for a Domain Name System (DNS) request, metadata associated with the DNS request, and the action taken (e.g., permit/forward, deny/block, redirect, and log behaviors) are logged. Activities associated with other (application-layer) protocols (e.g., Dynamic Host Configuration Protocol (DHCP), Domain Name System (DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Internet Message Access Protocol (IMAP), Post Office Protocol (POP), Secure Shell (SSH), Secure Sockets Layer (SSL), Transport Layer Security (TLS), telnet, Remote Desktop Protocol (RDP), Server Message Block (SMB), and the like) and their respective metadata may be additionally or alternatively logged. For example, metadata further includes at least one of a source (IP) address and/or hostname, a source port, destination (IP) address and/or hostname, a destination port, protocol, application, and the like.

FIG. 3 depicts environment 300 according to various embodiments. Environment 300 includes hardware 310, host operating system 320, container engine 330, and containers 340 ₁-340 _(z). In some embodiments, hardware 310 is implemented in at least one of physical hosts 160 _(1,1)-160 _(x,y) (FIG. 1). Host operating system 320 runs on hardware 310 and can also be referred to as the host kernel. By way of non-limiting example, host operating system 320 can be at least one of: Linux, Red Hat® Enterprise Linux® Atomic Enterprise Platform, CoreOS®, Ubuntu® Snappy, Pivotal Cloud Foundry®, Oracle® Solaris, and the like. Host operating system 320 allows for multiple (instead of just one) isolated user-space instances (e.g., containers 340 ₁-340 _(z)) to run in host operating system 320 (e.g., a single operating system instance).

Host operating system 320 can include a container engine 330. Container engine 330 can create and manage containers 340 ₁-340 _(z), for example, using an (high-level) application programming interface (API). By way of non-limiting example, container engine 330 is at least one of Docker®, Rocket (rkt), Solaris Containers, and the like. For example, container engine 330 may create a container (e.g., one of containers 340 ₁-340 _(z)) using an image. An image can be a (read-only) template comprising multiple layers and can be built from a base image (e.g., for host operating system 320) using instructions (e.g., run a command, add a file or directory, create an environment variable, indicate what process (e.g., application or service) to run, etc.). Each image may be identified or referred to by an image type. In some embodiments, images (e.g., different image types) are stored and delivered by a system (e.g., server side application) referred to as a registry or hub (not shown in FIG. 3).

Container engine 330 can allocate a filesystem of host operating system 320 to the container and add a read-write layer to the image. Container engine 330 can create a network interface that allows the container to communicate with hardware 310 (e.g., talk to a local host). Container engine 330 can set up an Internet Protocol (IP) address for the container (e.g., find and attach an available IP address from a pool). Container engine 330 can launch a process (e.g., application or service) specified by the image (e.g., run an application, such as one of APP 350 ₁-350 _(z), described further below). Container engine 330 can capture and provide application output for the container (e.g., connect and log standard input, outputs and errors). The above examples are only for illustrative purposes and are not intended to be limiting.

Containers 340 ₁-340 _(z) can be created by container engine 330. In some embodiments, containers 340 ₁-340 _(z), are each an environment as close as possible to an installation of host operating system 320, but without the need for a separate kernel. For example, containers 340 ₁-340 _(z) share the same operating system kernel with each other and with host operating system 320. Each container of containers 340 ₁-340 z can run as an isolated process in user space on host operating system 320. Shared parts of host operating system 320 can be read only, while each container of containers 340 ₁-340 _(z) can have its own mount for writing.

Containers 340 ₁-340 _(z) can include one or more applications (APP) 350 ₁-350 _(z) (and all of their respective dependencies). APP 350 ₁-350 _(z) can be any application or service. By way of non-limiting example, APP 350 ₁-350 _(z) can be a database (e.g., Microsoft® SQL Server®, MongoDB, HTFS, etc.), email server (e.g., Sendmail®, Postfix, qmail, Microsoft® Exchange Server, etc.), message queue (e.g., Apache® Qpid™, RabbitMQ®, etc.), web server (e.g., Apache® HTTP Server™, Microsoft® Internet Information Services (IIS), Nginx, etc.), Session Initiation Protocol (SIP) server (e.g., Kamailio® SIP Server, Avaya® Aura® Application Server 5300, etc.), other media server (e.g., video and/or audio streaming, live broadcast, etc.), file server (e.g., Linux server, Microsoft® Windows Server®, etc.), service-oriented architecture (SOA) and/or microservices process, object-based storage (e.g., Lustre®, EMC® Centera®, Scality® RING®, etc.), directory service (e.g., Microsoft® Active Directory®, Domain Name System (DNS) hosting service, etc.), and the like.

Each of VMs 260 ₁-260 _(V) (FIG. 2) and containers 340 ₁-340 _(z) can be referred to as workloads and/or endpoints. In contrast to hypervisor-based virtualization VMs 260 ₁-260 _(V), containers 340 ₁-340 _(z) may be an abstraction performed at the operating system (OS) level, whereas VMs are an abstraction of physical hardware. Since VMs 260 ₁-260 _(V) can virtualize hardware, each VM instantiation of VMs 260 ₁-260 _(V) can have a full server hardware stack from virtualized Basic Input/Output System (BIOS) to virtualized network adapters, storage, and central processing unit (CPU). The entire hardware stack means that each VM of VMs 260 ₁-260 _(V) needs its own complete OS instantiation and each VM must boot the full OS.

FIG. 4 illustrates environment 400, according to some embodiments. Environment 400 can include one or more of enforcement point 250, environments 300 ₁-300 _(W), orchestration layer 410, and metadata 430. Enforcement point 250 can be an enforcement point as described in relation to enforcement points 250 ₁-250 _(V) (FIG. 2). Environments 300 ₁-300 _(W) can be instances of environment 300 (FIG. 3), include containers 340 _(1,1)-340 _(W,Z), and be in at least one of data center 120 (FIG. 1). Containers 340 _(1,1)-340 _(W,Z) (e.g., in a respective environment of environments 300 ₁-300 _(W)) can be a container as described in relation to containers 340 ₁-340 _(Z) (FIG. 3).

Orchestration layer 410 can manage and deploy containers 340 _(1,1)-340 _(W,Z) across one or more environments 300 ₁-300 _(W) in one or more data centers of data center 120 (FIG. 1). In some embodiments, to manage and deploy containers 340 _(1,1)-340 _(W,Z), orchestration layer 410 receives one or more image types (e.g., named images) from a data storage and content delivery system referred to as a registry or hub (not shown in FIG. 4). By way of non-limiting example, the registry can be the Google Container Registry. In various embodiments, orchestration layer 410 determines which environment of environments 300 ₁-300 _(W) should receive each container of containers 340 _(1,1)-340 _(W,Z) (e.g., based on the environments' 300 ₁-300 _(W) current workload and a given redundancy target). Orchestration layer 410 can provide means of discovery and communication between containers 340 _(1,1)-340 _(W,Z). According to some embodiments, orchestration layer 410 runs virtually (e.g., in one or more containers 340 _(1,1)-340 _(W,Z) orchestrated by a different one of orchestration layer 410 and/or in one or more of hypervisor 230 (FIG. 2)) and/or physically (e.g., in one or more physical hosts of physical hosts 160 _(1,1)-160 _(x,y) (FIG. 1) in one or more of data center 120. By way of non-limiting example, orchestration layer 410 is at least one of Docker Swarm®, Kubernetes®, Cloud Foundry® Diego, Apache® Mesos™, and the like.

Orchestration layer 410 can maintain (e.g., create and update) metadata 430. Metadata 430 can include reliable and authoritative metadata concerning containers (e.g., containers 340 _(1,1)-340 _(W,Z)). By way of non-limiting example, metadata 430 indicates for a container at least one of: an image name (e.g., file name including at least one of a network device (such as a host, node, or server) that contains the file, hardware device or drive, directory tree (such as a directory or path), base name of the file, type (such as format or extension) indicating the content type of the file, and version (such as revision or generation number of the file), an image type (e.g., including name of an application or service running), the machine with which the container is communicating (e.g., IP address, host name, etc.), and a respective port through which the container is communicating, and other tag and/or label (e.g., a (user-configurable) tag or label such as a Kubernetes® tag, Docker® label, etc.), and the like.

In various embodiments, metadata 430 is generated by orchestration layer 410—which manages and deploys containers—and can be very timely (e.g., metadata is available soon after an associated container is created) and highly reliable (e.g., accurate). Other metadata may additionally or alternatively comprise metadata 430. By way of non-limiting example, metadata 430 includes an application determination using application identification (AppID). AppID can process data packets at a byte level and can employ signature analysis, protocol analysis, heuristics, and/or behavioral analysis to identify an application and/or service. In some embodiments, AppID inspects only a part of a data payload (e.g., only parts of some of the data packets). By way of non-limiting example, AppID is at least one of Cisco Systems® OpenAppID, Qosmos ixEngine®, Palo Alto Networks® APP-ID™, and the like.

Enforcement point 250 can receive metadata 430, for example, through application programming interface (API) 420. Other interfaces can be used to receive metadata 430.

FIG. 5 illustrates system 500 according to some embodiments. System 500 can include analytics engine 510, scanlets 520 ₁-520 _(A), data store 530, and enforcement points 250 ₁-250 _(B). Enforcement points 250 ₁-250 _(B) can at least be enforcement points as described in relation to enforcement points 250 ₁-250 _(V) (FIG. 2) and 250 (FIG. 4). Analytics engine 510 can receive and store metadata from enforcement points 250 ₁-250 _(B). For example, metadata from enforcement points 250 ₁-250 _(B) includes at least one of a source (IP) address and/or hostname, a source port, destination (IP) address and/or hostname, a destination port, protocol, application, username and/or other credentials used to gain access to computing resources on a network, number of bytes in communication between client-server and/or server-client, and the like. By way of further non-limiting example, metadata from enforcement point 250 ₁-250 _(B) includes metadata 430 (FIG. 4).

In various embodiments, analytics engine 510 stores metadata in and receives metadata from data store 530. Data store 530 can be a repository for storing and managing collections of data such as databases, files, and the like, and can include a non-transitory storage medium (e.g., mass data storage 930, portable storage device 940, and the like described in relation to FIG. 9). Analytics engine 510 can be at least one of a physical host 160 _(1,1)-160 _(x,y) (FIG. 1), VM 260 ₁-260 _(V) (FIG. 2), container 340 ₁-340 _(z) (FIG. 3), the like, and combinations thereof, in the same server (rack), different server (rack), different data center, and the like than scanlets 520 ₁-520 _(A), data store 530, enforcement points 250 ₁-250 _(B), and combinations thereof. In some embodiments, analytics engine 510 comprises scanlets 520 ₁-520 _(A).

Scanlets 520 ₁-520 _(A) are each designed/optimized to detect malicious activity, such as network enumeration (e.g., discovering information about the data center, network, etc.), vulnerability analysis (e.g., identifying potential ways of attack), and exploitation (e.g., attempting to compromise a system by employing the vulnerabilities found through the vulnerability analysis), for a particular protocol and/or application. By way of non-limiting example, one of scanlets 520 ₁-520 _(A) is used to detect malicious activity in network communications using a particular protocol, such as (application-layer) protocols (e.g., Dynamic Host Configuration Protocol (DHCP), Domain Name System (DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Internet Message Access Protocol (IMAP), Post Office Protocol (POP), Secure Shell (SSH), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc.). By way of further non-limiting example, one of scanlets 520 ₁-520 _(A) is used to detect malicious activity in network communications using a certain application, such as applications described in relation to applications 280 ₁-280 _(V) (FIG. 2) and applications 350 ₁-350 _(z) (FIG. 3).

In some embodiments, scanlets 520 ₁-520 _(A) are each a computer program, algorithm, heuristic function, hash, other (binary) string/pattern/signature, the like, and combinations thereof, which are used to detect malicious behavior. As vulnerabilities/exploits are discovered for a particular protocol and/or application, for example, by information technology (IT) staff responsible for security in a particular organization, outside cyber security providers, “white hat” hackers, academic/university researchers, and the like, the scanlet of scanlets 520 ₁-520 _(A) associated with the particular protocol and/or application may be hardened (e.g., updated to detect the vulnerability/exploit).

By way of non-limiting example, a scanlet of scanlets 520 ₁-520 _(A) associated with SMB is used to detect and/or analyze: certain syntax (e.g., psexec, which can be used for remote process execution), encryption, n-gram calculation of filenames, behavioral analytics on the source and/or destination nodes, and usage by the source and/or destination nodes of the SMB protocol. By way of further non-limiting example, a scanlet of scanlets 520 ₁-520 _(A) associated with DNS is used to detect and/or analyze: n-gram calculation on a dns-query, TTL (Time to Live) value (e.g., analytics on normal value), response code (e.g., analytics on normal value), and usage by the source and/or destination nodes of the DNS protocol. By way of additional non-limiting example, a scanlet of scanlets 520 ₁-520 _(A) associated with RDP is used to detect and/or analyze: known bad command, encryption, connection success rates, and usage by the source and/or destination nodes of the DNS protocol.

In this way, scanlets 520 ₁-520 _(A) can be used to detect such malicious behaviors as when: a node which usually acts as a client (e.g., receives data) begins acting as a server (e.g., sends out data), a node begins using a protocol which it had not used before, commands known to be malicious are found inside communications (e.g., using at least partial packet inspection), and the like. By way of further non-limiting example, scanlets 520 ₁-520 _(A) can be used to spot when: (known) malware is uploaded to a (network) node, user credentials are stolen, a server is set-up to steal user credentials, credit card data is siphoned off, and the like.

In operation, a scanlet of scanlets 520 ₁-520 _(A) examines metadata from network communications, where the network communications use the protocol or application for which the scanlet is designed/optimized. When the examination of a particular instance of network communication is completed, the scanlet provides a risk score denoting a likelihood or confidence level the network communication was malicious. In various embodiments, the risk score is a range of numbers, where one end of the range indicates low/no risk and the other end indicates high risk. By way of non-limiting example, the risk score ranges from 1-10, where 1 denotes low/no risk and 10 denotes high risk. Other ranges of numbers (e.g., including fractional or decimal numbers) can be used. Low/no risk can be at either end of the range, so long as high risk is consistently at the opposite end of the range from low/no risk.

Risk scores generated by scanlets 520 ₁-520 _(A) can be normalized to the same or consistent range of numbers. For example, scanlets 520 ₁-520 _(A) can each return risk scores normalized to the same range of numbers and/or analytic engine 510 can normalize all risk scores received from scanlets 520 ₁-520 _(A) (e.g., some of scanlets 520 ₁-520 _(A) may produce risk scores using a different range of numbers than others of scanlets 520 ₁-520 _(A)) to the same range of numbers.

When malicious activity is suspected in a particular (network) node (referred to as a “primary node”), communications to and from the node can be examined for malicious activity. For example, a network node may be deemed to be suspicious, for example, because IT staff noticed suspicious network activity and/or a warning is received from a virus scanner or other malware detector, a cyber security provider, firewall 130 (FIG. 1), enforcement point 250 (250 ₁-250 _(V) and/or 250 ₁-250 _(B); FIGS. 2, 4, and 5), or other security mechanism. A (network) node can be at least one of a physical host 160 _(1,1)-160 _(x,y) (FIG. 1), VM 260 ₁-260 _(V) (FIG. 2), container 340 ₁-340 _(z) (FIG. 3), client system, other computing system on a communications network, and combinations thereof.

In some embodiments, analytics engine 510 retrieves metadata stored in data store 530 concerning communications to and from the node. For example, the metadata can include: a source (IP) address and/or hostname, a source port, destination (IP) address and/or hostname, a destination port, protocol, application, username and/or other credentials used to gain access, number of bytes in communication between client-server and/or server-client, metadata 430 (FIG. 4), and the like. By way of further non-limiting example, the amount of metadata retrieved can be narrowed to a certain time period (e.g., second, minutes, days, weeks, months, etc.) using a (user-configurable) start date, start time, end date, end time, and the like. Since a given protocol may be used on the order of tens of thousands of times per minute in a data center, narrowing the metadata received can be advantageous.

For each communication into or out of the node, analytics engine 510 can identify a protocol and/or application used. Using the identified protocol and/or application, analytics engine 510 can apply a scanlet of scanlets 520 ₁-520 _(A) associated with the protocol and/or a scanlet associated with the application. Analytics engine 510 compares the risk score returned from the scanlet of scanlets 520 ₁-520 _(A). When the risk score exceeds a (user-configurable) pre-determined first threshold, the communication and its source or destination (referred to as a “secondary node”) can be identified as malicious. The first threshold can be a whole and/or fractional number. Depending of the range of values of the risk score and which end of the range is low or high risk, the risk score can deceed a (user-configurable) pre-determined threshold to identify the communication and its source or destination as potentially malicious. The secondary node identified as malicious and an identifier associated with the malicious communication can be stored. Additionally, other information such as the risk score, time stamp, direction (e.g., source and destination), description of the malicious behavior, and the like may also be stored.

In some embodiments, once communications to and from the primary node are examined, analytics engine 510 recursively examines communications to and from the malicious secondary nodes using appropriate scanlets of scanlets 520 ₁-520 _(A). Recursive examination can be similar to the examination described above for communications to and from the primary node, where a result of the immediately preceding examination is used in the present examination. For example, analytics engine 510 retrieves metadata stored in data store 530 concerning communications to and from the malicious secondary nodes, narrowed to a particular time period. By way of further example, when the risk score exceeds a (user-configurable) pre-determined first threshold, the communication and its source or destination (referred to as a “tertiary node”) can be identified as malicious. The tertiary node identified as malicious, an identifier associated with the malicious communication, and/or other information can be stored (e.g., in data store 530). Examination of communications to or from the primary node (e.g., communications to which a scanlet has already been applied and/or for which a risk score has already been received) can be skipped to prevent redundancy.

Recursive examination can continue as described above to examine communications to and from the malicious tertiary nodes (then quaternary nodes, quinary nodes, senary nodes, etc.). For each iteration, when the risk score exceeds a (user-configurable) pre-determined threshold, the communication and its source or destination can be identified as malicious. The node identified as malicious, an identifier associated with the malicious communication, and/or other information can be stored (e.g., in data store 530). Communications to which a scanlet has already been applied and/or for which a risk score has already been received may be skipped to prevent redundancy.

In some embodiments, the recursive examination continues until a particular (user-defined) depth or layer is reached. For example, the recursive examination stops once the secondary nodes (or tertiary nodes or quaternary nodes or quinary nodes or senary nodes, etc.) are examined. By way of further non-limiting example, the recursive examination stops once at least one of: a (user-defined) pre-determined number of malicious nodes is identified, a (user-defined) pre-determined amount of time for the recursive examination, is reached. In various embodiments, the recursive examination continues until a limit is reached. For example, after multiple iterations new nodes (and/or communications) are not present in the metadata retrieved, because they were already examined at a prior level or depth. In this way, the recursive examination can be said to have converged on a set of malicious nodes (and communications).

Information about communications not deemed malicious (e.g., identifier, nodes communicating, risk score, time stamp, direction, description of malicious behavior, and the like for each communication), such as when the risk score is not above the (user-defined) predetermined threshold (referred to as a first threshold), may also be stored (e.g., in data store 530). In some embodiments, all communications examined by analytics engine 510 are stored. In various embodiments, communications having risk scores “close” to the first threshold but not exceeding the first threshold are stored. For example, information about communications having risk scores above a second threshold but lower than the first threshold can be stored (e.g., in data store 530) for subsequent holistic analysis.

Throughout the recursive examination described above, at least some of the communications originating from or received by a particular node may not exceed the first (user-defined) predetermined threshold and the node is not identified as malicious. However, two or more of the communications may have been “close” to the first predetermined threshold. For example, the communications have risk scores above a second threshold but lower than the first threshold.

Although individual communications may not (be sufficient to) indicate the node (and individual communications) are malicious, a group of communications “close” to the threshold can be used to identify the node (and individual communications) as malicious. For example, if a number of communications having risk scores above the second threshold but below the first threshold (referred to as “marginally malicious”) exceeds a (user-defined) predetermined threshold (referred to as a “third threshold”), then the node (and the marginally malicious communications) are identified as malicious. The second threshold can be a whole and/or fractional number.

In some embodiments, all nodes have a risk score. As described in relation to risk scores for communications, risk scores for nodes can be any range of numbers and meaning. For example, as each communication a particular node participates in receives a normalized risk score from a scanlet and the risk score for the node is an average (e.g., arithmetic mean, running or rolling average, weighted average where each scanlet is assigned a particular weight, and the like) of the communication risk scores. By way of further non-limiting example, when the average exceeds a (user-defined) predetermined second threshold, then the node is identified as malicious.

The node identified as malicious, identifiers associated with the malicious communications, a notation that the malicious node (and malicious communications) were identified using a number of marginally malicious communications or node risk score, and/or other information can be stored (e.g., in data store 530).

FIGS. 6A and 6B illustrate a method 600 for recursive examination according to some embodiments. Method 600 may be performed at least in part using analytics engine 510 (FIG. 5). At step 610, a first identifier for a first node can be received. The first identifier for the first node can be received from a user, virus scanner or other malware detector, a cyber security provider, firewall 130 (FIG. 1), point 250 (250 ₁-250 _(V) and/or 250 ₁-250 _(B); FIGS. 2, 4, and 5), or other security mechanism. The first identifier can be an IP address, hostname, and the like. At step 615, first metadata associated with communications received and/or provided by the first node can be retrieved. For example, communications which occurred during a user-defined period of time can be retrieved from data store 530.

At step 620, at least one second node and at least one associated communication in communication with the first node is identified, for example, using the retrieved metadata. The at least one second node can be identified using an identifier such as an IP address, hostname, and the like. At step 625, a characteristic of each identified communication is ascertained. For example, the characteristic is a protocol and/or application used in the communication.

At step 630, each of the identified communications are examined for malicious behavior, for example, using a scanlet of scanlets 520 ₁-520 _(A). For example, using the ascertained characteristic, a scanlet of scanlets 520 ₁-520 _(A) associated with the characteristic is selected for the examination and applied to the communication. At step 635, a risk score associated with each communication is received, for example, from the applied scanlet of scanlets 520 ₁-520 _(A).

At step 640, second nodes are identified as malicious. For example, when a risk score exceeds a predetermined threshold, the associated node (and corresponding communication) is identified as malicious.

At step 645, each of the second nodes can be recursively analyzed. For example, steps 615 through 640 are performed for each of the second nodes and third nodes are identified as malicious. The third nodes can be identified using an identifier such as an IP address, hostname, and the like. Also at step 645, each of the third nodes can be recursively analyzed. For example, steps 615 through 640 are performed for each of the third nodes, and fourth nodes are identified as malicious. Further at step 645, each of the fourth nodes can be recursively analyzed. For example, steps 615 through 640 are performed for each of the fourth nodes, and fifth nodes are identified as malicious. And so on, until a limit is reached (step 650).

At step 650, a check to see if the limit is reached can be performed. For example, a limit is a (user-configurable) depth, level, or number of iterations; period of time for recursive examination; number of malicious nodes identified; when the set of malicious nodes converges; and the like. When a limit is not reached, another recursion or iteration can be performed (e.g., method 600 recursively repeats step 645). When a limit is reached, method 600 can continue to step 660). Steps 645 and 650 (referred to collectively as step 655) are described further with respect to FIGS. 7A and 7B.

At step 660, additional nodes are optionally identified as malicious. For example, a node is identified as malicious when a number of marginally malicious communications (as described above in relation to FIG. 5) associated with the node exceeds a predetermined limit.

At step 665, the malicious nodes (and associated malicious communications) can be generated and provided. For example, a report including the malicious nodes and associated communications is produced. A non-limiting example of a graphical representation of the malicious nodes and their communication relationships is described further with respect to FIG. 8.

At step 670, remediation can be optionally performed. Using the recursive multi-layer examination described above in relation to FIGS. 5-7B, remediation can block the particular breach/attack, as well as similar breaches/attacks using the same or similar methodology, when it is still in progress. For example, remediation includes at least one of: quarantining particular nodes (e.g., certain nodes are not allowed to communicate with each other), re-imaging particular nodes, banning particular applications and/or protocols from a particular data center or network (e.g., when there is no business rationale to permit it), updating a security policy (which allowed the breach/attack to occur), and the like.

In some embodiments, a security policy can be low-level (e.g., firewall rules, which are at a low level of abstraction and only identify specific machines by IP address and/or hostname). Alternatively or additionally, a security policy can be at a high-level of abstraction (referred to as a “high-level declarative security policy”). A high-level security policy can comprise one or more high-level security statements, where there is one high-level security statement per allowed protocol, port, and/or relationship combination. The high-level declarative security policy can be at least one of: a statement of protocols and/or ports the primary physical host, VM, or container is allowed to use, indicate applications/services that the primary physical host, VM, or container is allowed to communicate with, and indicate a direction (e.g., incoming and/or outgoing) of permitted communications.

A high-level security policy can comprise one or more high-level security statements, where there is one high-level security statement per allowed protocol, port, and/or relationship combination. The high-level declarative security policy can be at least one of: a statement of protocols and/or ports the primary VM or container is allowed to use, indicate applications/services that the primary VM or container is allowed to communicate with, and indicate a direction (e.g., incoming and/or outgoing) of permitted communications.

The high-level declarative security policy is at a high level of abstraction, in contrast with low-level firewall rules, which are at a low level of abstraction and only identify specific machines by IP address and/or hostname. Accordingly, one high-level declarative security statement can be compiled to produce hundreds or more of low-level firewall rules. The high-level security policy can be compiled by analytics engine 510 (FIG. 5), enforcement point 250 (250 ₁-250 _(V) and/or 250 ₁-250 _(B); FIGS. 2, 4, and 5), or other machine to produce a low-level firewall rule set. Compilation is described further in related United States Patent Application “Conditional Declarative Policies” (application Ser. No. 14/673,640) filed Mar. 30, 2015, which is hereby incorporated by reference for all purposes.

FIGS. 7A and 7B show further example details for step 655 (FIG. 6B) performing two recursions or iterations, according to some embodiments. At step 721, second metadata, associated with second communications received or provided by each malicious second node, can be retrieved. For example, second communications which occurred during a user-defined period of time can be retrieved from data store 530.

At step 722, at least one third node (and corresponding second communication) in communication with each malicious second node is identified, for example, using the retrieved second metadata. At step 723, a second characteristic of each identified second communication is ascertained. For example, the characteristic is a protocol and/or application used in the second communication.

At step 724, each of the identified second communications are examined for malicious behavior, for example, using a scanlet of scanlets 520 ₁-520 _(A) (FIG. 5). For example, using the ascertained second characteristic, a scanlet of scanlets 520 ₁-520 _(A) associated with the second characteristic is selected for the examination and applied. At step 725, a risk score associated with each second communication is received, for example, from the applied scanlet of scanlets 520 ₁-520 _(A).

At step 726, third nodes are identified as malicious. For example, when a risk score exceeds a predetermined threshold, the associated third node (and corresponding second communication) are identified as malicious.

At step 727, a determination is made that a limit has not been reached. For example, step 650 (FIG. 6B) determines a limit is not reached. A limit may be a (user-configurable) depth, level, or number of iterations; period of time for recursive examination; number of malicious nodes identified; when the set of malicious nodes converges; and the like. In some embodiments, when a limit is not reached, another iteration or recursion is performed. Since the example of FIGS. 7A and 7B supposes two recursions or iterations, the process depicted in FIGS. 7A and 7B continues to step 731.

At step 731, third metadata associated with second communications received or provided by each malicious third node can be retrieved. For example, third communications which occurred during a user-defined period of time can be retrieved from data store 530.

At step 732, at least one fourth node (and corresponding third communication) in communication with each malicious third node is identified, for example, using the retrieved third metadata. The at least one fourth node can be identified using an identifier such as an IP address, hostname, and the like. At step 733, a third characteristic of each identified third communication is ascertained. For example, the third characteristic is a protocol and/or application used in the third communication.

At step 734, each of the identified third communications are examined for malicious behavior, for example, using a scanlet of scanlets 520 ₁-520 _(A). For example, using the ascertained third characteristic, a scanlet of scanlets 520 ₁-520 _(A) associated with the third characteristic is selected for the examination and applied. At step 735, a risk score associated with each third communication is received, for example, from the applied scanlet of scanlets 520 ₁-520 _(A).

At step 736, fourth nodes are identified as malicious. For example, when a risk score exceeds a predetermined threshold, the associated fourth node (and corresponding third communication) are identified as malicious.

At step 737, a determination is made that a limit has been reached. For example, step 650 (FIG. 6B) determines a limit is reached. A limit may be a (user-configurable) depth, level, or number of iterations; period of time for recursive examination; number of malicious nodes identified; when the set of malicious nodes converges; and the like. In some embodiments, when a limit is reached, another iteration or recursion is not performed. Since the example of FIGS. 7A and 7B supposes two recursions or iterations, the process depicted in FIGS. 7A and 7B continues to step 731.

While two iterations or recursions are depicted in FIGS. 7A and 7B, other numbers of recursions or iterations can be used to reach a limit. In various embodiments, a check (e.g., step 650 in FIG. 6B) is performed after each iteration (e.g., steps 727 and 737) to determine if the limit is reached. When a limit is not reached, another recursion or iteration can be performed. When a limit is reached, another recursion or iteration may not be performed and the process shown in FIGS. 7A and 7B continues to step 660 of method 600 (FIG. 6B).

FIG. 8 is a graphical representation (graph) 800 of the malicious nodes and their communication relationships. Each malicious node is represented (plotted) by a point (e.g., nodes 820 _(1-A)) in graph 800. Each malicious communication is represented (drawn) by a line or edge (e.g., communications 830 _(1-B)) in graph 800. Properties such as a respective protocol, application, statistical properties (e.g., number of incoming/outgoing bytes), risk score, other description of why the communication was deemed malicious, direction (e.g., inbound, outbound, lateral, etc.), other metadata, and the like can be associated with each of communications 830 _(1-B). By way of non-limiting example, node 820 ₃ shows properties such as an (IP) address, and application or protocol associated with communication 830 ₃. By way of further non-limiting example, each of nodes 820 _(1-A) can be colored (not depicted in FIG. 8), such that each application and/or protocol is indicated by a particular color. In some embodiments, graph 800 may be provided interactively, such that when receiving an indication or selection from a user (e.g., hovering a pointer over (and optionally clicking on), touching on a touchscreen, and the like on a particular point or edge), properties associated with the indicated node(s) and/or communication(s) are displayed (e.g., in a pop-up window, balloon, infobar, sidebar, status bar, etc.).

In some embodiments, a node may be represented on graph 800 more than once. By way of non-limiting example, node 820 ₂ having (IP) address 10.10.0.10 is represented by 820 ₂₍₁₎ and 820 ₂₍₁₎. A particular network may have only one node having address 10.10.0.10, and 820 ₂₍₁₎ and 820 ₂₍₁₎ refer to the same node. 820 ₂₍₁₎ and 820 ₂₍₁₎ can be dissimilar in that that each has a different malicious communication with node 820 ₁. For example, 820 ₂₍₁₎ is in communication with node 820 ₁ through communication 830 ₁, and 820 ₂₍₂₎ is in communication with node 820 ₁ through communication 830 ₂. Here, communication 830 ₁ is outbound from 820 ₁ using telnet, and communication 830 ₂ is inbound to 820 ₁ using RDP.

As shown in FIG. 8, progress 840 of an intrusion via instances of malicious communications/behavior (e.g., infection sometimes referred to as a “kill chain”) over time is depicted beginning at a left side and proceeding to a right side of FIG. 8. Accordingly, the first node subject to an intrusion (e.g., infection, compromise, etc.) is depicted by a nodes 820 _(L1) and 820 _(L2), being farthest to the left in FIG. 8. Here, more than one node (e.g., nodes 820 _(L1) and 820 _(L2)) may be the origin of an intrusion or security breach. Nodes 820 _(L1) and 820 _(L2) may be referred to as the “primary case” or “patient zero.” Point 820 ₁ is the primary node (e.g., starting node of the recursive examination described above in relation to FIGS. 5-7). Depending on the circumstances of a particular kill chain, node 820 ₁ and at least one of nodes 820 _(L1) and 820 _(L2) can be the same point or different points.

FIG. 9 illustrates an exemplary computer system 900 that may be used to implement some embodiments of the present invention. The computer system 900 in FIG. 9 may be implemented in the contexts of the likes of computing systems, networks, servers, or combinations thereof. The computer system 900 in FIG. 9 includes one or more processor unit(s) 910 and main memory 920. Main memory 920 stores, in part, instructions and data for execution by processor unit(s) 910. Main memory 920 stores the executable code when in operation, in this example. The computer system 900 in FIG. 9 further includes a mass data storage 930, portable storage device 940, output devices 950, user input devices 960, a graphics display system 970, and peripheral device(s) 980.

The components shown in FIG. 9 are depicted as being connected via a single bus 990. The components may be connected through one or more data transport means. Processor unit(s) 910 and main memory 920 are connected via a local microprocessor bus, and the mass data storage 930, peripheral device(s) 980, portable storage device 940, and graphics display system 970 are connected via one or more input/output (I/O) buses.

Mass data storage 930, which can be implemented with a magnetic disk drive, solid state drive, or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit(s) 910. Mass data storage 930 stores the system software for implementing embodiments of the present disclosure for purposes of loading that software into main memory 920.

Portable storage device 940 operates in conjunction with a portable non-volatile storage medium, such as a flash drive, floppy disk, compact disk, digital video disc, or Universal Serial Bus (USB) storage device, to input and output data and code to and from the computer system 900 in FIG. 9. The system software for implementing embodiments of the present disclosure is stored on such a portable medium and input to the computer system 900 via the portable storage device 940.

User input devices 960 can provide a portion of a user interface. User input devices 960 may include one or more microphones, an alphanumeric keypad, such as a keyboard, for inputting alphanumeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. User input devices 960 can also include a touchscreen. Additionally, the computer system 900 as shown in FIG. 9 includes output devices 950. Suitable output devices 950 include speakers, printers, network interfaces, and monitors.

Graphics display system 970 include a liquid crystal display (LCD) or other suitable display device. Graphics display system 970 is configurable to receive textual and graphical information and processes the information for output to the display device.

Peripheral device(s) 980 may include any type of computer support device to add additional functionality to the computer system.

The components provided in the computer system 900 in FIG. 9 are those typically found in computer systems that may be suitable for use with embodiments of the present disclosure and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 900 in FIG. 9 can be a personal computer (PC), hand held computer system, telephone, mobile computer system, workstation, tablet, phablet, mobile phone, server, minicomputer, mainframe computer, wearable, or any other computer system. The computer may also include different bus configurations, networked platforms, multi-processor platforms, and the like. Various operating systems may be used including UNIX, LINUX, WINDOWS, MAC OS, PALM OS, QNX ANDROID, IOS, CHROME, and other suitable operating systems.

Some of the above-described functions may be composed of instructions that are stored on storage media (e.g., computer-readable medium). The instructions may be retrieved and executed by the processor. Some examples of storage media are memory devices, tapes, disks, and the like. The instructions are operational when executed by the processor to direct the processor to operate in accord with the technology. Those skilled in the art are familiar with instructions, processor(s), and

In some embodiments, the computing system 900 may be implemented as a cloud-based computing environment, such as a virtual machine operating within a computing cloud. In other embodiments, the computing system 900 may itself include a cloud-based computing environment, where the functionalities of the computing system 900 are executed in a distributed fashion. Thus, the computing system 900, when configured as a computing cloud, may include pluralities of computing devices in various forms, as will be described in greater detail below.

According to some embodiments, the present disclosure is directed to providing network visualization features. These features can be used to display a plurality of network nodes in a given network. The plurality of network nodes can comprise a grouping of computing devices (physical and virtual machines), microsegmented services, network applications, and so forth.

The visualization of the network allows for illustration of one or more layers of affinity between the network nodes, where affinity between nodes generally is indicative of communication between nodes. The affinity can also include other communicative parameters such as communication frequency, for example.

Visualization of the network is provided in an interactive graphical user interface (GUI) format, where a user can manipulate and interact with the network mapping of nodes. Users are provided with a robust amount of information that allows a user to quickly determine key metrics regarding their network. Such actions include which systems, devices, applications, services, and so forth, are interacting and communicating with one another. Additionally, detailed information about individual nodes and communications between nodes are accessible through interactions with the GUI.

In some embodiments, a user can select a cyber security policy to apply to the network. The application of the cyber security policy causes visual changes in the appearance of the GUI. For example, nodes or communication paths that are non-compliant with the cyber security policy are changed to have a visually distinctive appearance as compared to those nodes which are compliant. This allows a user to quickly assess and identify network nodes that may cause data breaches or other cyber security failures.

In some embodiments, rather than or in addition to examining the GUI for nodes or links that appear not to comply with a cyber security policy, a ruleset can be used to identify potential malicious activity occurring on the network and or through its nodes. For example, the ruleset can be used to locate nodes that are making numerous outbound connections to a server in a foreign location (e.g., based on IP address information). In another example, the ruleset can include acceptable network protocols that can be used by a node in the network. One example includes the use of application layer protocol analysis, where it is given that node A is authorized to use only SMTP (simple mail transfer protocol). In this example, the use of TFTP (trivial file transfer protocol) by node A would cause node A to be indicated as potentially problematic on the GUI. Other similar rules can be generated for the ruleset that is applied during the creation of the GUI.

According to some embodiments, the user can manipulate the GUI through manipulation of nodes and/or links between nodes in order to resolve compliance issues with respect to the cyber security policy. As compliance is achieved, the representations will change in appearance. The changes to the network resulting from the user's interactions with the GUI can be replicated at the network level to improve the performance and/or security of the network. In some embodiments, a cyber security policy can also be updated based on the user's interaction with the GUI of their network.

Some embodiments include a graphics and physics engine that maximizes a zoom level for the GUI based on the nodes present in the GUI and the display size available for the GUI. The display size can be based on a browser display window size or other display constraint.

FIG. 10 is a flowchart of an example method for generating a GUI of the present disclosure.

The method begins with a step 1002 of receiving a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network. These IP addresses can be a range of IP addresses or Classless Inter-Domain Routing (CIDR) range that identifies locations of assets within a network. The query can also specify a single IP address or selections of IP addresses not necessarily tied to a specific range of IP addresses.

In one embodiment, the IP address range belongs to network nodes that provide a particular application or function using a similar application layer protocol. In another example, the IP address range can correspond to nodes within a network that are required to be PCI or HIPAA compliant.

Once the IP addresses have been identified from the query, characteristics of the nodes identified by the IP addresses are obtained in step 1004. It will be understood that a node can include any network device, either virtual or physical, that exists in a network. This could include virtual machines, physical machines, applications, services, containers, microsegmented features, and so forth. Any object on the network performing computational and/or communicative activities can be selected.

The characteristics can be obtained using the metadata processes described in greater detail above, or using any known method for evaluating attributes/characteristics of network nodes that would be known to one of ordinary skill in the art with the present disclosure before them.

Next, the method includes a step 1006 of determining communications between the nodes and communications between the nodes and any other nodes not included in the selection of IP addresses. Thus, communications between nodes identified by the IP addresses specified in the query are located. Secondly, communications between nodes identified by the IP addresses and other nodes that are not identified by the IP addresses specified in the query are located.

The communications information is converted into affinity information. A primary affinity represents communication between the nodes included on the IP address list. This is a first order affinity between nodes of the same application type.

A secondary affinity represents communication between the nodes and the other nodes not included in the selection of IP addresses. For example, a group of containers that provide a specific service, such as mail service, is specified on an IP address list. Communications between these containers and any other network objects would be identified as a secondary affinity. This type of communication is a second order affinity.

Using this primary and secondary affinity information, and the node characteristics, an interactive graphical user interface (GUI) can be generated.

Thus, the method includes a step 1008 of generating a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses. To be sure, the nodes chosen through IP address selection may be only a portion of an entire network. In other instances, the entire network can be mapped.

The generation of the GUI includes a step 1010 of placing links between the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity.

Specific details regarding the GUIs are provided infra and described to a greater extent with reference to FIGS. 11 and 12.

The method can also include a step 1012 of providing the graphical user interface to a user.

FIG. 11 illustrates an example GUI 1100 that includes a plurality of nodes. The nodes 1102A-D are nodes which are associated with IP address values in a query. Thus, these are the primary nodes which are being graphed and analyzed. Links, such as link 1104, extending between nodes, indicate that communication has occurred between the nodes. For example, node 1102A and node 1102B have communicated with one another, causing link 1104 to be placed between node 1102A and node 1102B. For ease of visualization, nodes can be grouped in shaded or bounded areas. For example, nodes 1102A-D are located in a bounded area 1106. A bounded area can be indicative of nodes using a similar application protocol type or a geographical colocation of nodes. For example, nodes 1108A-B are located in a foreign country as compared to the nodes 1102A-D. Another node group includes nodes 1110A-C, which can be located in the same cloud service as nodes 1102A-D.

In sum, the nodes 1102A-D match IP addresses included in the query, whereas nodes 1108A-B and 1110A-C do not match and/or do not fall within the IP addresses included in the query.

Once the nodes have been mapped and graphed, a user can now utilize the GUI 1100 to learn more about the nodes and the links. For example, the user can select node 1102C and a text box is generated that includes all the characteristics obtained for the node. Links can also be clicked for more information. For example, clicking link 1104 can generate a text box that includes an indication of how many times the nodes have communicated, the direction of communications (e.g., in-bound/out-bound relative to which node), and so forth. In one example, the node 1102A has placed five outbound messages to node 1102B.

The GUI 1100 also allows for differentiation of edge nodes. An edge node is a node that communicates with nodes that are outside a given boundary. For example, node 1102C is an edge node that communicates with node 1108B, and node 1102D is an edge node that communicates with node 1110B, which is outside the boundary 1106.

The affinity attributes described above between two or more nodes is also indicated visually by proximity between nodes on the GUI 1100. For example, nodes 1102A and 1102B were found to have communicated more frequently than between nodes 1102A and 1102C or between 1102B and 1102C. Thus, nodes 1102A and 1102B are displayed closer to one another within the GUI 1100. Again, the communications between any of nodes 1102A-D are indications of primary affinity whereas communication between any of nodes 1102A-D and nodes outside of boundary 1106 are considered links of secondary affinity.

In one embodiment, a cyber security policy or network ruleset is applied to further enhance the information conveyed using the GUI 1100. For example, when a cyber security policy is applied, it is determined that node 1102D is communicating with node 1110B, which is outside the IP address range specified in the query or request. In some instances, this may be allowable, but because in this particular instance the nodes 1102A-D are VMs that process PCI sensitive data, such as credit card information, any access to these nodes requires node 1110E to be in compliance with PCI standards (e.g., subject to PCI rules). This is an example of a cyber security-based policy application that changes the appearance of the GUI 1100. The GUI 1100 is changed to update the appearance of node 1102D and node 1110E to indicate that a possible cyber security issue exists. Node 1102D is triangular rather than circular as with the other nodes in the group.

In a ruleset-based example, a ruleset is applied that references communication with network resources that are located in a foreign country. In another example, the node 1108B could be on an IP blacklist. Regardless, communication between the node 1102C and 1108B is impermissible and the GUI 1100 is updated to illustrate that the node 1108B is a diamond, which indicates that it is a suspected malicious network resource.

Again, rather than using differing shapes, the GUI can include changes in color or other visual indicators to create visual distinctiveness. In general, the application of a cyber security policy and/or network ruleset to the information mapped to a GUI results in visual distinctiveness between nodes that are compliant and nodes that are non-compliant with the cyber security policy and/or network ruleset.

In some embodiments, the user can manipulate or otherwise interact with the GUI 1100 to resolve some of these cyber security and/or ruleset issues. FIG. 12 illustrates an updated version of GUI 1100 as GUI 1200. The user has removed both the linkage between node 1102D and 1110B, as well as node 1102D, and the link between node 1102C and node 1108B. The user has also move the node 1102C around within the grouping.

The removal of the link between node 1102C and 1108B can result in the updating of a cyber security policy for the network that no new connections with the IP associated with node 1108B is permissible. This can also be implemented as a ruleset modification.

The removal of the links and of node 1102D causes the termination of the VM associated with node 1102D. For example, removal of the node 1102D can send a message to a hypervisor that manages nodes 1102A-D to shut down any communications between node 1102D and 1110E and terminate the node 1102D.

Thus, the manipulation of the GUI 1100 causes corresponding changes to the network relative to nodes 1102A-D which are reflected in GUI 1200. The user need not be required to know how to implement a node termination for the node to be terminated. The user need only understand how to use the GUI to effect a corresponding change at the network level.

In some embodiments, a user can rearrange node representations within a GUI for various reasons. When a user has moved node representations into close proximity with one another, the portion of the GUI that includes these nodes can be collapsed such that multiple nodes collapse into a single node to reduce visual cluttering of the GUI.

In some embodiments, the system can effectuate node assignment within a GUI of the present disclosure. For example, in some embodiments, the system can ensure that no two nodes within the GUI overlap one another. In one embodiment, the node could be assigned a pixel or a grouping of pixels within the GUI. The pixel could be an anchor point to which the node and its representation is linked. Thus, when the GUI is generated, the representation of the node is displayed by anchoring the representation to the pixel(s) linked to the node.

The system then defines where a pixel should be placed in the GUI for optimal viewability and interactability within the GUI. For example, the system can determine that a plurality of nodes needs to be displayed, so the system optimally arranges the nodes (which are located within the GUI using pixels of the GUI) such that they are displayed at their largest optimal size within the GUI while avoiding overlap between the representations of the nodes.

In some embodiments, the system defines spacing between nodes so that the representation is set at a maximum zoom level while all remaining contextual information, such as links or text, are also viewable. Thus, in some embodiments, the automatic sizing or resizing is constrained so that labeling or textual content of a representation of one or more nodes is not obscured by another node in the GUI.

FIGS. 13 and 14 collectively illustrate the automatic resizing of a GUI 1300 based on user manipulation of the nodes therein. A query results in the generation of GUI 1300. The user desires to focus on only boundaries 1302 and 1304 and thus removes boundaries 1306, 1308, and 1310. The GUI 1300 is displayed within a display area 1312 that is of a given dimension, such as a browser window.

When removed, the GUI 1300 is automatically resized to enlarge the nodes remaining in the GUI 1300 to fit the dimensions of the browser window. In this example, a corresponding increase in size of the nodes and links results from the resizing.

FIG. 15 is a simplified block diagram of an example system that can be used to practice aspects of the present disclosure. The visualization system 1500 generally comprises a processor 1501 that is configured to execute various modules and engines of the visualization system 1500 such as a query processing module 1502 that receives a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network.

The visualization system 1500 also comprises a data gathering module 1504 that obtains characteristics for the nodes and determines communications between the nodes and communications between the nodes and any other nodes not included in the selection of IP addresses. This information is used to determine affinity information between nodes.

An affinity analysis module 1506 is used that determines a primary affinity indicative of communication between the nodes and a secondary affinity indicative of communication between the nodes and the other nodes not included in the selection of IP addresses.

In some embodiments, the visualization system 1500 also comprises a graphics engine 1508 that generates a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses. The graphics engine 1508 also places links between the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity. In some embodiments, the graphics engine 1508 provides the graphical user interface to a user. For example, the GUI can be provided through a browser window of web interface or a browser executing on an end user client device.

In some embodiments, the visualization system 1500 also comprises a physics engine 1510 that automatically sizes the GUI based on a display size in such a way that all nodes are included in the display size at a maximum zoom value even when the nodes are moved around the GUI by the user.

According to some embodiments, the visualization system 1500 also comprises a security module 1512 that applies at least one of a cyber security policy or a network ruleset. In some embodiments, the security module 1512 alters representations for nodes that fail to comply with a cyber security policy or the network ruleset such that the representations are visually distinct compared to the nodes that comply with the cyber security policy or the network ruleset.

In one or more embodiments, the security module 1512 receives user input that comprises any of selection, movement, editing, and deletion of either one of the nodes or one of the links. The security module 1512 also generates or updates a cyber security policy based on the user input, and in some instances applies the cyber security policy to the network.

In general, a cloud-based computing environment is a resource that typically combines the computational power of a large grouping of processors (such as within web servers) and/or that combines the storage capacity of a large grouping of computer memories or storage devices. Systems that provide cloud-based resources may be utilized exclusively by their owners or such systems may be accessible to outside users who deploy applications within the computing infrastructure to obtain the benefit of large computational or storage resources.

The cloud is formed, for example, by a network of web servers that comprise a plurality of computing devices, such as the computing system 900, with each server (or at least a plurality thereof) providing processor and/or storage resources. These servers manage workloads provided by multiple users (e.g., cloud resource customers or other users). Typically, each user places workload demands upon the cloud that vary in real-time, sometimes dramatically. The nature and extent of these variations typically depends on the type of business associated with the user.

It is noteworthy that any hardware platform suitable for performing the processing described herein is suitable for use with the technology. The terms “computer-readable storage medium” and “computer-readable storage media” as used herein refer to any medium or media that participate in providing instructions to a CPU for execution. Such media can take many forms, including, but not limited to, non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical, magnetic, and solid-state disks, such as a fixed disk. Volatile media include dynamic memory, such as system random-access memory (RAM).

Transmission media include coaxial cables, copper wire and fiber optics, among others, including the wires that comprise one embodiment of a bus. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, any other physical medium with patterns of marks or holes, a RAM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a Flash memory, any other memory chip or data exchange adapter, a carrier wave, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a CPU for execution. A bus carries the data to system RAM, from which a CPU retrieves and executes the instructions. The instructions received by system RAM can optionally be stored on a fixed disk either before or after execution by a CPU.

Computer program code for carrying out operations for aspects of the present technology may be written in any combination of one or more programming languages, including an object oriented programming language such as JAVA, SMALLTALK, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

Aspects of the present technology are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present technology. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 

What is claimed is:
 1. A method, comprising: receiving a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network; obtaining characteristics for the nodes; determining communications between the nodes and communications between the nodes and any other nodes not included in the selection of IP addresses; determining a primary affinity indicative of the communications between the nodes and a secondary affinity indicative of the communications between the nodes and the other nodes not included in the selection of IP addresses, the primary affinity and the secondary affinity further indicative of a frequency of communications between nodes; generating a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses; placing links between the representations of the nodes in the selection of IP addresses and the representations of the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity; providing the GUI to a user; applying at least one of a cyber security policy or a network ruleset; altering the representations for nodes that fail to comply with the cyber security policy or the network ruleset such that the representations are visually distinct compared to the nodes that comply with the cyber security policy or the network ruleset; receiving user input associated with either one of the nodes or one of the links; and sending a message in response to the user input, the message including instructions to bring at least one of the nodes that fail to comply with the cyber security policy or the network ruleset into compliance with the cyber security policy or the network ruleset.
 2. The method according to claim 1, in which the GUI is configured such that the representations of the nodes in the selection of IP addresses and the representations of the other nodes not included in the selection of IP addresses are included in a display of the GUI at a maximum zoom value.
 3. The method according to claim 2, in which the GUI is selectively altered to remove representations of nodes inside one or more boundaries based on user manipulation of the one or more boundaries.
 4. The method according to claim 1, in which the nodes in the selection of IP addresses are associated with a same application.
 5. The method according to claim 1, further comprising: receiving a selection of one of the nodes; and displaying the characteristics for the node.
 6. The method according to claim 1, further comprising: receiving a selection of one of the links; and displaying details of the communications including communication protocol information, packet information, and communication frequency.
 7. The method according to claim 1, in which: a proximity of the representations of the nodes in the selection of IP addresses to one another in the GUI is based on the primary affinity; and a proximity of the representations of the other nodes not included in the selection of IP addresses to the representations of the nodes in the selection of IP addresses in the GUI is based on the secondary affinity.
 8. The method according to claim 1, in which the altering the representations includes color coding any of the representations of the nodes or the links based on compliance of the nodes or the links with the cyber security policy or the network ruleset.
 9. The method according to claim 1, further comprising: generating an updated cyber security policy based on the user input, the user input comprising any of selection, movement, and deletion; and applying the updated cyber security policy to the network.
 10. The method according to claim 9, in which the updated cyber security policy indicates that communications between the nodes is forbidden in response to a user removing a link between the nodes.
 11. The method according to claim 1, in which the communications determined are selected from a timeframe included in the query.
 12. The method according to claim 1, in which the representations of the nodes in the selection of IP addresses are visually distinct from the representations of the other nodes not included in the selection of IP addresses.
 13. The method according to claim 1, in which the nodes in the selection of IP addresses are each associated with a layer 7 application protocol that processes sensitive information.
 14. The method according to claim 1, in which at least a portion of the nodes are edge nodes and the representations of the edge nodes are visually distinct from the remaining nodes.
 15. A system, comprising: a query processing module that: receives a query that comprises a selection of Internet protocol (IP) addresses belonging to nodes within a network; a data gathering module that: obtains characteristics for the nodes; and determines communications between the nodes and communications between the nodes and any other nodes not included in the selection of IP addresses; an affinity analysis module that: determines a primary affinity indicative of the communications between the nodes and a secondary affinity indicative of the communications between the nodes and the other nodes not included in the selection of IP addresses, the primary affinity and the secondary affinity further indicative of a frequency of communications between nodes; a graphics engine that: generates a graphical user interface (GUI) that comprises representations of the nodes in the selection of IP addresses and the other nodes not included in the selection of IP addresses; places links between the representations of the nodes in the selection of IP addresses and the representations of the other nodes not included in the selection of IP addresses based on the primary affinity and the secondary affinity; and provides the GUI to a user; and a security module that: applies at least one of a cyber security policy or a network ruleset; receives user input associated with either one of the nodes or one of the links; and sends a message in response to the user input, the message including instructions to bring at least one of the nodes that fail to comply with the cyber security policy or the network ruleset into compliance with the cyber security policy or the network ruleset.
 16. The system according to claim 15, further comprising a physics engine that generates the GUI based on a display size in such a way that all nodes are included in the display size at a maximum zoom value even when the nodes are moved around the GUI by the user.
 17. The system according to claim 15, in which the security module further: alters the representations for nodes that fail to comply with the cyber security policy or the network ruleset such that the representations are visually distinct compared to the nodes that comply with the cyber security policy or the network ruleset.
 18. The system according to claim 15, in which the security module further: generates an updated cyber security policy based on the user input, the user input comprising any of selection, movement, editing, and deletion; and applies the updated cyber security policy to the network. 